SDK Quickstart

Use this path if you are integrating LiteLLM directly into application code.

1. Install LiteLLM

uv add 'litellm==1.82.6'

2. Set Provider Credentials

Start with one provider and set its environment variables.

OpenAI: OPENAI_API_KEY
Anthropic: ANTHROPIC_API_KEY
Azure OpenAI: AZURE_API_KEY, AZURE_API_BASE, AZURE_API_VERSION
Bedrock: standard AWS credentials
Vertex AI: VERTEXAI_PROJECT, VERTEXAI_LOCATION

If you have not picked a provider yet, browse all supported providers.

3. Make Your First Call

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-api-key"

response = completion(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
)

print(response.choices[0].message.content)

4. Check The Response

The line below:

print(response.choices[0].message.content)

prints the assistant text, for example:

Hello! I'm doing well, thanks for asking.

If you print the full object with:

print(response)

you will see a Python ModelResponse(...) object. For an OpenAI-backed model, it can look like this:

ModelResponse(
    id='chatcmpl-abc123',
    created=1773782130,
    model='gpt-4o-2024-08-06',
    object='chat.completion',
    system_fingerprint='fp_4ff89bf575',
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content="Hello! I'm just a program, but I'm here to help you. How can I assist you today?",
                role='assistant',
                tool_calls=None,
                function_call=None,
                provider_specific_fields={'refusal': None},
                annotations=[]
            ),
            provider_specific_fields={}
        )
    ],
    usage=Usage(
        completion_tokens=21,
        prompt_tokens=13,
        total_tokens=34,
        completion_tokens_details=CompletionTokensDetailsWrapper(...),
        prompt_tokens_details=PromptTokensDetailsWrapper(...)
    ),
    service_tier='default'
)

The same response follows an OpenAI-style shape. Conceptually, it looks like this:

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1677858242,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm doing well, thanks for asking."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 12,
    "total_tokens": 25
  }
}

id, created, token counts, and message text will vary by request.

If you call an OpenAI-backed model, you may also see extra fields such as system_fingerprint, service_tier, tool_calls, function_call, annotations, provider_specific_fields, and detailed token usage. For the full output reference, see completion output.

Need more provider examples? See the main Getting Started page.